The secret motives and decisions of the play are generally confined to the wings where no prying eye or ear, including the spectator's, can perceive them. The stage then becomes an intermediary present between the past and another present happening out of sight but just as significant.
High quality-factor (Q) optical resonators are a key component for ultra-narrow linewidth lasers, frequency stabilization, precision spectroscopy and quantum applications. Integration in a photonic waveguide platform is key to reducing cost, size, power and sensitivity to environmental disturbances. However, to date, the Q of all-waveguide resonators has been relegated to below 260 Million. Here, we report a Si3N4 resonator with 422 Million intrinsic and 3.4 Billion absorption-limited Qs. The resonator has 453 kHz intrinsic, 906 kHz loaded, and 57 kHz absorption-limited linewidths and the corresponding 0.060 dB m−1 loss is the lowest reported to date for waveguides with deposited oxide upper cladding. These results are achieved through a careful reduction of scattering and absorption losses that we simulate, quantify and correlate to measurements. This advancement in waveguide resonator technology paves the way to all-waveguide Billion Q cavities for applications including nonlinear optics, atomic clocks, quantum photonics and high-capacity fiber communications.
Methamphetamine abuse leads to devastating consequences, including addiction, crime, and death. Despite decades of research, no medication has been approved by the U.S. Food and Drug Administration for the treatment of Methamphetamine Use Disorder. Thus, there is a need for new therapeutic approaches. Animal studies demonstrate that methamphetamine exposure dysregulates forebrain function involving the Group-I metabotropic glutamate receptor subtype 5 (mGlu5), which is predominantly localized to postsynaptic sites. Allosteric modulators of mGlu5 offer a unique opportunity to modulate glutamatergic neurotransmission selectively, thereby potentially ameliorating methamphetamine-induced disruptions. Negative allosteric modulators of mGlu5 attenuate the effects of methamphetamine, including rewarding/reinforcing properties of the drug across animal models, and have shown promising effects in clinical trials for Anxiety Disorder and Major Depressive Disorder. Preclinical studies have also sparked great interest in mGlu5 positive allosteric modulators, which exhibit antipsychotic and anxiolytic properties, and facilitate extinction learning when access to methamphetamine is removed, possibly via the amelioration of methamphetamine-induced cognitive deficits. Clinical research is now needed to elucidate the mechanisms underlying the mGlu5 receptor-related effects of methamphetamine and the contributions of these effects to addictive behaviors. The growing array of mGlu5 allosteric modulators provides excellent tools for this purpose and may offer the prospect of developing tailored and effective medications for Methamphetamine Use Disorder.
This paper uses movement as a marker to study interactions in humans and animals to better understand their collective behaviors. Interaction is an important driving force in social and ecological systems. It can also play a significant role in the transmission of infectious diseases and viruses as witnessed during the ongoing COVID-19 pandemic. Although a number of approaches have been developed to analyze interaction using movement data sets, these methods mainly capture concurrent and dyadic interaction (i.e. when two individuals have direct contact or move synchronously in the spatial proximity of each other). Less work has been done on tracing interaction between multiple individuals, especially when the interaction occurs with a delay or via indirect contact (i.e. when individuals visit the same location asynchronously). This paper introduces a new Object-oRiented Time-Geographic Analytical approach (ORTEGA) to extract concurrent and delayed interaction patterns between individuals in space and time. The method leverages the time-geography framework to incorporate the effects of uncertainty and gaps in movement data in the analysis of interaction and tracing contact patterns. Using two different case studies and real GPS tracking data, the method is evaluated in (1) detecting patterns of dyadic, intra and interspecific interactions between two apex predators, tigers and leopards in Thailand; and (2) tracing potential contacts between a large group of individuals of the same and different households in San Jose, California. The results indicate that tigers and leopards have an awareness of each other and their interaction is mainly indirect and delayed. In the human context, the results show that while individuals of the same household have more concurrent interaction, members of different households follow similar patterns asynchronously exhibiting delayed interaction. The delayed interactions and potential asynchronous contacts are often underestimated by the common digital contact tracing technologies. With this study we show how a generic method can be used to identify interesting movement patterns across the human and animal divide.
The COVID-19 outbreak is asynchronous in US counties. Mitigating the COVID-19 transmission requires not only the state and federal level order of protective measures such as social distancing and testing, but also public awareness of time-dependent risk and reactions at county and community levels. We propose a robust approach to estimate the heterogeneous progression of SARS-CoV-2 at all US counties having no less than 2 COVID-19 associated deaths, and we use the daily probability of contracting (PoC) SARS-CoV-2 for a susceptible individual to quantify the risk of SARS-CoV-2 transmission in a community. We found that shortening by [Formula: see text] of the infectious period of SARS-CoV-2 can reduce around [Formula: see text] (or 78 K, [Formula: see text] CI: [66 K , 89 K ]) of the COVID-19 associated deaths in the US as of 20 September 2020. Our findings also indicate that reducing infection and deaths by a shortened infectious period is more pronounced for areas with the effective reproduction number close to 1, suggesting that testing should be used along with other mitigation measures, such as social distancing and facial mask-wearing, to reduce the transmission rate. Our deliverable includes a dynamic county-level map for local officials to determine optimal policy responses and for the public to better understand the risk of contracting SARS-CoV-2 on each day.
Macrophages destroy pathogens and diseased cells through Fcγ receptor (FcγR)-driven phagocytosis of antibody-opsonized targets. Phagocytosis requires activation of multiple FcγRs, but the mechanism controlling the threshold for response is unclear. We developed a DNA origami-based engulfment system that allows precise nanoscale control of the number and spacing of ligands. When the number of ligands remains constant, reducing ligand spacing from 17.5 nm to 7 nm potently enhances engulfment, primarily by increasing efficiency of the engulfment-initiation process. Tighter ligand clustering increases receptor phosphorylation, as well as proximal downstream signals. Increasing the number of signaling domains recruited to a single ligand-receptor complex was not sufficient to recapitulate this effect, indicating that clustering of multiple receptors is required. Our results suggest that macrophages use information about local ligand densities to make critical engulfment decisions, which has implications for the mechanism of antibody-mediated phagocytosis and the design of immunotherapies.
We introduce an approach based on the Givens representation for posterior
inference in statistical models with orthogonal matrix parameters, such as
factor models and probabilistic principal component analysis (PPCA). We show
how the Givens representation can be used to develop practical methods for
transforming densities over the Stiefel manifold into densities over subsets of
Euclidean space. We show how to deal with issues arising from the topology of
the Stiefel manifold and how to inexpensively compute the change-of-measure
terms. We introduce an auxiliary parameter approach that limits the impact of
topological issues. We provide both analysis of our methods and numerical
examples demonstrating the effectiveness of the approach. We also discuss how
our Givens representation can be used to define general classes of
distributions over the space of orthogonal matrices. We then give
demonstrations on several examples showing how the Givens approach performs in
practice in comparison with other methods.